Document dataframe parameter in custom_stoploss().
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Rokas Kupstys
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@ -42,33 +42,6 @@ class AwesomeStrategy(IStrategy):
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***
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### Storing custom information using DatetimeIndex from `dataframe`
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Imagine you need to store an indicator like `ATR` or `RSI` into `custom_info`. To use this in a meaningful way, you will not only need the raw data of the indicator, but probably also need to keep the right timestamps.
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```python
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import talib.abstract as ta
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class AwesomeStrategy(IStrategy):
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# Create custom dictionary
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custom_info = {}
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# using "ATR" here as example
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dataframe['atr'] = ta.ATR(dataframe)
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if self.dp.runmode.value in ('backtest', 'hyperopt'):
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# add indicator mapped to correct DatetimeIndex to custom_info
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self.custom_info[metadata['pair']] = dataframe[['date', 'atr']].set_index('date')
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return dataframe
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```
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!!! Warning
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The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
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!!! Note
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If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
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See `custom_stoploss` examples below on how to access the saved dataframe columns
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## Custom sell signal
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It is possible to define custom sell signals. This is very useful when we need to customize sell conditions for each individual trade.
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@ -107,7 +80,8 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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current_rate: float, current_profit: float, dataframe: Dataframe,
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**kwargs) -> float:
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"""
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Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
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e.g. returning -0.05 would create a stoploss 5% below current_rate.
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@ -157,7 +131,8 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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current_rate: float, current_profit: float, dataframe: DataFrame,
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**kwargs) -> float:
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# Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
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if current_time - timedelta(minutes=120) > trade.open_date_utc:
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@ -183,7 +158,8 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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current_rate: float, current_profit: float, dataframe: DataFrame,
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**kwargs) -> float:
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if pair in ('ETH/BTC', 'XRP/BTC'):
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return -0.10
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@ -209,7 +185,8 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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current_rate: float, current_profit: float, dataframe: DataFrame,
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**kwargs) -> float:
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if current_profit < 0.04:
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return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss
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@ -249,7 +226,8 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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current_rate: float, current_profit: float, dataframe: DataFrame,
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**kwargs) -> float:
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# evaluate highest to lowest, so that highest possible stop is used
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if current_profit > 0.40:
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@ -266,14 +244,20 @@ class AwesomeStrategy(IStrategy):
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Imagine you want to use `custom_stoploss()` to use a trailing indicator like e.g. "ATR"
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See: "Storing custom information using DatetimeIndex from `dataframe`" example above) on how to store the indicator into `custom_info`
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!!! Warning
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only use .iat[-1] in live mode, not in backtesting/hyperopt
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otherwise you will look into the future
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Only use `dataframe` values up until and including `current_time` value. Reading past
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`current_time` you will look into the future, which will produce incorrect backtesting results
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and throw an exception in dry/live runs.
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see [Common mistakes when developing strategies](strategy-customization.md#common-mistakes-when-developing-strategies) for more info.
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!!! Note
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DataFrame is indexed by candle date. During dry/live runs `current_time` and
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`trade.open_date_utc` will not match candle dates precisely and using them as indices will throw
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an error. Use `date = timeframe_to_prev_date(self.timeframe, date)` to round a date to previous
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candle before using it as a `dataframe` index.
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``` python
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from freqtrade.exchange import timeframe_to_prev_date
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from freqtrade.persistence import Trade
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from freqtrade.state import RunMode
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@ -284,28 +268,19 @@ class AwesomeStrategy(IStrategy):
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use_custom_stoploss = True
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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current_rate: float, current_profit: float, dataframe: DataFrame,
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**kwargs) -> float:
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result = 1
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if self.custom_info and pair in self.custom_info and trade:
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# using current_time directly (like below) will only work in backtesting.
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# so check "runmode" to make sure that it's only used in backtesting/hyperopt
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if self.dp and self.dp.runmode.value in ('backtest', 'hyperopt'):
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relative_sl = self.custom_info[pair].loc[current_time]['atr']
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# in live / dry-run, it'll be really the current time
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else:
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# but we can just use the last entry from an already analyzed dataframe instead
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dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
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timeframe=self.timeframe)
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# WARNING
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# only use .iat[-1] in live mode, not in backtesting/hyperopt
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# otherwise you will look into the future
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# see: https://www.freqtrade.io/en/latest/strategy-customization/#common-mistakes-when-developing-strategies
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relative_sl = dataframe['atr'].iat[-1]
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if (relative_sl is not None):
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# Using current_time directly would only work in backtesting. Live/dry runs need time to
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# be rounded to previous candle to be used as dataframe index. Rounding must also be
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# applied to `trade.open_date(_utc)` if it is used for `dataframe` indexing.
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current_time = timeframe_to_prev_date(self.timeframe, current_time)
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current_row = dataframe.loc[current_time]
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if 'atr' in current_row:
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# new stoploss relative to current_rate
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new_stoploss = (current_rate-relative_sl)/current_rate
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new_stoploss = (current_rate - current_row['atr']) / current_rate
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# turn into relative negative offset required by `custom_stoploss` return implementation
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result = new_stoploss - 1
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